Kounis syndrome (KS) describes the occurrence of acute coronary syndromes precipitated by allergic, hypersensitivity, or anaphylactic reactions and represents a unique intersection between immunologic activation and cardiovascular disease. The epidemiology of KS is likely underestimated due to diagnostic overlap with other cardiac and allergic conditions and limited awareness across medical specialties. This narrative review focuses on the distinctive features of KS in special populations, emphasizing how patients’ age, comorbidities, immune status, and vascular substrate modify presentation, diagnosis, and outcomes. In elderly patients, polypharmacy, increased plaque vulnerability, and endothelial dysfunction favor Type II and III KS. Pediatric cases, although rare, are predominantly Type I and strongly associated with food allergies, insect stings, vaccines, and antibiotics, with under-recognition driven by diagnostic bias and ethical concerns surrounding invasive testing. Patients with coronary stents, cardiac devices, chronic kidney disease, and those receiving dialysis exhibit heightened susceptibility due to chronic inflammation, foreign-body hypersensitivity, and prothrombotic states. Pregnancy and the peripartum period represent a unique immuno-hemodynamic milieu in which Th2 immune shift, increased coronary vasoreactivity, and obstetric triggers can compromise both maternal and fetal perfusion. Additional risk modulation is observed in atopic individuals, asthmatics, patients with autoimmune, inflammatory, oncologic, psychiatric, and neurodevelopmental conditions, as well as in COVID-19 and post-infectious states. We propose a host-modified framework for KS that complements traditional classification by integrating immune phenotype and vascular substrate, enabling improved risk stratification and personalized preventive strategies.

Introduction: Chronic kidney disease (CKD) is a progressive disorder characterized by a gradual loss of renal function and frequent disturbances in iron metabolism. These disturbances often lead to iron deficiency and anemia. Serum iron and ferritin are fundamental biomarkers for evaluating iron status and can provide valuable insights into disease severity and progression. The aim of this study was to determine if there is an association between CKD stage and serum iron concentration, focusing on the dynamics of iron homeostasis and CKD progression. Methods: At the Clinical Center of the University of Sarajevo, a cross-sectional study was conducted, including 115 participants diagnosed with CKD in moderate to advanced stages (3-5). Patients with acute infections, other causes of anemia, or acute kidney injury were excluded to avoid factors that could confound iron metabolism. Laboratory analyses included measurements of serum iron, ferritin, urea, and creatinine. Estimated glomerular filtration rate (eGFR) was calculated using the modification of diet in renal disease equation to determine disease stage. Descriptive statistics and correlation analyses were used to examine associations between serum iron, ferritin, and CKD stage, with statistical significance set at p < 0.05. Results: The study included 64% male and 36% female participants, with 17% in stage 3, 22% in stage 4, and 61% in stage 5 of the disease. As CKD progressed, serum ferritin levels increased, while serum iron levels gradually declined. Correlation analysis revealed a significant positive association between eGFR and serum iron (p < 0.01). The results also indicated that abnormal iron values were more frequent in males than in females, which is consistent with previous reports. Conclusion: The severity of CKD is associated with disturbances in iron metabolism, pointing to the importance of monitoringserum iron and ferritin. These findings support early detection, individualized patient management, and timely interventions to prevent iron deficiency and anemia in patients with CKD.

Chronic inflammatory diseases and autoimmune diseases are overlapping but distinct immune-mediated disorders that represent a growing worldwide health concern, characterised by persistent inflammation, tissue damage, and progressive organ dysfunction. In the United States alone, more than $180 billion is spent annually on managing these conditions, yet fewer than 10% of patients achieve long-term remission. These figures highlight the limitations of conventional therapies, which often control symptoms rather than adequately modify the underlying disease process. This review provides a focused and comparative overview of emerging therapeutic strategies across representative immune-mediated disorders, with particular emphasis on mesenchymal stem cells, Janus kinase-signal transducer and activator of transcription (JAK-STAT) inhibitors, chimeric antigen receptor T-cell therapies, therapeutic vaccines, microbiome-modulating interventions, and nanotechnology-based drug delivery systems. In parallel, artificial intelligence (AI) is increasingly contributing to biomarker discovery, drug repurposing, and treatment stratification, thereby supporting the development of predictive and personalised medicine. Overall, these advances support a shift toward mechanism-based, multimodal, and more durable treatment strategies, although further clinical validation remains necessary.

Recent advances in nanoelectronics have spurred increased interest in the human brain and its complex functions. Numerous studies have explored brain behavior in varying levels of detail, from individual neurons to entire lobes. Intricately structured, the brain is a complex organ susceptible to diseases that may disrupt the connectivity between its internal regions. Investigating this phenomenon, the present study applies a discrete finite-state model to map the behavior of neurons within a neuronal agglomerate and examine the effect of disease on these behaviors. Each agglomerate is then compared to a wireless clustered network and modeled as a finite-state system, with inter-cluster communications analyzed under conditions of temporal variations and degradation. This work represents one of the most advanced applications of discrete finite-state processes and routing theory in brain modeling.

This paper presents an ongoing initiative titled "TransAC" (Transnational Academy), a project aimed at mitigating brain drain from rural and crisis-stricken regions in the Danube area through innovative, inclusive, and human-centered training programs. TransAC addresses the dual challenge of demographic decline and skill mismatch by establishing a transnational training academy focused on additive manufacturing (AM). Targeting both young learners at risk of migration and vulnerable populations, the project fosters equitable access to digital and technical education, enhances regional resilience, and promotes sustainable development through stakeholder co-creation. Key outputs include a pilot training center, the establishment of a transnational academy, and direct training of 572 learners. The approach integrates advanced education technologies and social work perspectives, with emphasis on FabLabs, micro-credentials, and blended learning environments.

This paper presents the conceptual design of an immersive virtual escape-room system for training a broad range of soft skills, including teamwork, communication, collaborative problem-solving, time management, leadership, adaptability, situational awareness, and decision-making under pressure. The system consists of two integrated components: (1) a multi-user VR environment in which participants must coordinate actions and solve interdependent tasks under time constraints, and (2) a moderator interface enabling real-time supervision, scenario control, performance annotation, and structured debriefing. The design followed an iterative, user-centered methodology involving requirements analysis, definition of learning outcomes, narrative and puzzle design, interaction prototyping, and preliminary pilot testing with student teams. Early observations suggest that the VR environment elicits authentic teamwork behaviors, supports high engagement, and provides a psychologically safe and repeatable training context. Future work will include empirical evaluation of learning effectiveness across specific soft-skill domains, refinement of facilitator workflows, and integration of behavioral analytics for semi-automated performance assessment.

Background/Objectives: Coronary artery disease (CAD) remains the leading cause of death worldwide. Traditional cardiovascular risk assessment is based on chronological age and other clinical factors, with inherent limitations and poor accuracy. Objective was to estimate the artificial intelligence (AI)-enhanced biological cardiovascular age calculation derived from coronary computed tomography angiography (CTA) reports using a large language model (LLM), in predicting major adverse cardiovascular events (MACE). Methods: Coronary CTA reports were analyzed using a LLM (ChatGPT-4.0v, OpenAI), from symptomatic patients with suspected CAD who underwent coronary CTA for clinical indications. Patients in which the LLM successfully analyzed the key metrics (1) coronary artery calcium (CAC) score and (2) coronary CTA reports (coronary stenosis severity (CAD-RADS), high-risk anatomy, non-calcified plaque, cardiac function (LVEF and others) were included. Results: 386 CTA reports were uploaded, and 346 (89.6%) included. The mean biological age (bioAGE) was 57.2 ± 10.9 and the chronological 58.5 ± 10.8 years. 137 (39.6%) were women. The intra-individual deviation in bioAGE was high (median: 8.8; IQR 9.98). BioAGE exceeded chronological age in 45.4% patient and was lower or equal in 54.6%) MACE rate was 8.7% comprising 2 deaths, 5 myocardial infarctions, and 22 late revascularizations. The accuracy for prediction of MACE was higher for bioAGE (c = 0.768; 95% CI: 0.681–0.855, p < 0.001) compared to chronological age (c = 0.590; 95% CI: 0.492–0.689, p = 0.102) Conclusions: Biological age calculation from coronary CTA reports using LLM is feasible, yet intra-individual deviations are high. The accuracy for prediction of MACE is improved by bioAGE compared to chronological.

Background Sarajevo Canton reported large measles outbreaks in 2019 and 2024, highlighting the impact of the persistent gaps in immunisation coverage. Aim To analyse 2 measles outbreaks in Sarajevo Canton in Bosnia and Herzegovina, identify populations at risk and assess the impact of vaccination coverage on disease transmission. Methods We collected publicly available weekly case counts data for 45 weeks from the Public Health Institute of Sarajevo Canton and examined the vaccination coverage for 5 years to assess the impact of immunisation on outbreak dynamics. We conducted descriptive analyses using RStudio version 2024 and evaluated the differences between outbreaks using Mann-Whitney U test. P < 0.05 was considered statistically significant. Results A total of 869 cases were reported in 2019 and 4505 in 2024, and children aged 1-4 years were mostly affected (42.1%). Most of the cases were either unvaccinated or had unknown vaccination status; 92.3% of cases in 2019 were unvaccinated, and 87.7% in 2024 were unvaccinated, while 9.9% had unknown vaccination status. The 2024 outbreak had a higher and longer peak (416 vs 91 cases) occurence than 2019, and one death was reported in each year. Conclusion The declining vaccination coverage in Sarajevo Canton contributed to increased measles incidence. Strengthening mandatory immunisation, targeted catch-up campaigns and public communication are essential to achieve herd immunity, prevent future outbreaks and progress towards Universal Health Coverage.

Background Recent research highlights the pivotal role of gut microbiota and bile acids as modulators of metabolic homeostasis in type 2 diabetes (T2D). The concomitant use of probiotics and ursodeoxycholic acid (UDCA) may potentiate glycemic and lipid control via complementary mechanisms. Objective To evaluate the metabolic effects of probiotic supplementation and its combination with UDCA in metformin-treated T2D patients. Methods In this monocentric, prospective, randomized, double-blind, controlled trial, 90 patients with T2D on metformin therapy were randomized into three groups: metformin-only (MG), metformin plus probiotic (MPG), and metformin plus probiotic plus UDCA (MPUG). The intervention lasted 4 weeks. Primary outcomes included changes in fasting glucose, postprandial glucose and HbA1c. Secondary outcomes included lipid profile, C-reactive protein (CRP), and fecal levels of probiotics and UDCA. Two visits were conducted during the study - at the beginning and at the end. Visits involved patient interviews, clinical data collection, anthropometric measurements, blood biochemical analyses, and stool sample analysis for the presence of probiotic culture and UDCA concentrations. Results After 4 weeks, the MPUG group showed a significant reduction in fasting glucose (−1.7 mmol/L; 95% CI: −2.2 to −1.2), postprandial glucose (−1.3 mmol/L; 95% CI: −1.8 to −0.7), and HbA1c (−0.49%; 95% CI: −0.66 to −0.31) compared to the MG group. Total cholesterol and LDL cholesterol were also significantly reduced, while HDL increased. The concentration of Lactobacillus rhamnosus GG was highest in the MPUG group. No serious adverse events were reported. Conclusion Co-administration of probiotics and UDCA for four weeks in metformin-treated T2D patients significantly improves short-term glycemic control and lipid profiles. These promising results warrant validation in larger, longer-term clinical trials.

Tractometry, also known as tract profiling, is a powerful technique for probing microstructural properties along white matter (WM) tracts. A prerequisite for tractography-based tractometry is bundle parcellation-the subdivision of WM bundles into smaller segments where microstructural measures can be computed. However, existing parcellation methods lack consistency across bundles and timepoints, which reduces reproducibility and limits their utility for both longitudinal and cross-sectional studies. Moreover, these methods typically depend on tractography and bundle segmentation, two processes that are computationally expensive and often highly variable. In this work, we introduce BundleParc, a consistent and tractography-free bundle parcellation method. Instead of relying on streamline generation, BundleParc maps fiber orientation distribution function (fODF) volumes directly to label maps. Rigorous evaluation on research and clinical cohorts show that BundleParc is not only much simpler than state-of-the-art tract-based profiling methods, it is also consistently more accurate, robust and reproducible. With these results, BundleParc is a new solution for fast, easy-to-use, and off-the-shelf bundle segmentation and parcellation.

This paper deals with a time series of blackout events of the power transmission system in the Federation of Bosnia and Herzegovina (FB&H) in the period 2015–2023 and probability distributions that best fit these empirical data. The present study focuses on the global behaviour and dynamics of time series, ignoring the details of particular failures. We compiled the first comprehensive blackouts database in FB&H including the relevant information on each blackout feature. Most existing investigations concentrate on large-scale transmission systems, with limited evidence available for smaller and more compact grids. This study contributes new insight by investigating whether the heavy-tailed scaling observed in large systems also emerges in the FB&H transmission network. The power-law behaviour of the transmission network failure events in the FB&H power grid depending on their severity was identified. The estimated power-law exponent α is not merely a descriptive parameter but also reflects the underlying vulnerability of the system, making comparisons across countries valuable for assessing resilience and risk in transmission networks of different sizes and configurations. The results of this study show that the time intervals between the blackout events follow the exponential distribution. The data available from the FB&H power grid transmission centres in the observed period indicate that the frequency of all the blackout events in FB&H is not decreasing. It was determined that the blackout probability increases substantially during the morning hours, while the number of blackout events increases during the early summer and mid-fall months. However, the correlation analysis results show that there is only a weak, statistically insignificant correlation between blackout size and restoration time. The results for the FB&H power system, which relies predominantly on thermal power plants, indicate that despite differences in system scale and in the structure of electricity production compared with most EU power systems, no significant deviations are observed in blackout size behaviour when described by a power-law model. The results of this study provide a set of unique and valuable insights to operators and decision makers for the safe operation of the FB&H power system. The broader scope of applications of the results obtained includes the connection of the FB&H power system to power distribution across the Balkans for transfers and trading.

Efficient and consistent string processing is critical in the exponentially growing genomic data era. Locally Consistent Parsing (LCP) addresses this need by partitioning an input genome string into short, exactly matching substrings (“cores”), ensuring consistency across partitions. Compared to the popular sketching techniques, LCP produces fewer cores, enabling a more compact representation and faster analyses. Here, we present the first iterative implementation of LCP with Lcptools and introduce LCPan, an efficient variation graph constructor, which we show generates variation graphs >12×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} faster than vg, while using >13×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} less memory.

Predicting three-dimensional thermal loads during tokamak disruptions is essential for ITER yet remains weakly developed. We present a physics-based workflow that couples MHD simulations of vertical displacement events with field line tracing on a realistic 3D first wall model and a transient wall thermal response. The approach is validated against JET discharges with beryllium main chamber armour, reproducing key global dynamics, non-axisymmetric current features, and the occurrence (or absence) of melting, thereby building confidence in the methodology. We then apply the same workflow to ITER-relevant conditions with tungsten (W) armour, consistent with the new 2024 ITER re-baseline, to assess disruption heat loads and their 3D localization. The resulting analysis demonstrates the resilience of the ITER W first wall against these events and provides predictions for the energy deposition and current flow profiles. Beyond these studies, the workflow enables scenario-by-scenario estimates of disruption-induced thermal loading, allowing to assess the disruption-budget consumption for these events in future devices.

Background/Objectives: Clear cell renal cell carcinoma is the most common subtype of kidney cancer and exhibits marked biological heterogeneity, even among tumors of the same histological grade. Although tumor grade remains a key prognostic parameter, the molecular alterations associated with tumor differentiation are not fully understood. This study aimed to evaluate grade-dependent tissue-level expression patterns of proteins involved in cellular stress response, growth regulation, stemness, and apoptosis in clear cell renal cell carcinoma. Methods: Protein expression of heat shock protein 70, insulin-like growth factor 1, octamer-binding transcription factor 4, and apoptosis-inducing factor were analyzed in human clear cell renal cell carcinoma samples and normal renal cortex. Low-grade and high-grade tumors were compared using immunofluorescence staining combined with semi-quantitative and quantitative image analysis. The proportion of positive signals and the number of positive cells were assessed across tissue compartments. In addition, publicly available transcriptomic data from The Cancer Genome Atlas kidney renal clear cell carcinoma cohort were analyzed to explore associations between gene expression levels and overall survival. Results: Distinct grade-dependent expression patterns were observed for all investigated proteins. Heat shock protein 70, insulin-like growth factor 1, and octamer-binding transcription factor 4 showed a higher expression in normal renal tissue with a progressive reduction across tumor grades. In contrast, apoptosis-inducing factor exhibited increased expression in tumor tissue, particularly in low-grade tumors, with a relative decrease in high-grade carcinomas. Stromal compartments of tumor tissue showed minimal or no expression for most markers. Transcriptomic survival analysis did not reveal significant differences in overall survival between high- and low-expression groups for any of the investigated genes. Grade-stratified transcriptomic analysis of the TCGA KIRC cohort revealed consistent patterns for HSP70 family members and OCT4, with progressive grade-dependent mRNA reduction toward higher grades, while IGF1 showed an inverse mRNA trend and AIFM1 showed a uniform reduction across all tumor grades without a clear inter-grade pattern. Conclusions: The findings demonstrate that stress response, growth-related, stemness-associated, and apoptotic proteins display distinct grade-dependent tissue-level expression patterns in clear cell renal cell carcinoma, with the expression profiles of high-grade tumors being of particular translational interest given the aggressive clinical behavior and therapeutic resistance characteristic of this disease stage. These alterations appear to reflect tumor differentiation and biological behavior rather than independent prognostic value, highlighting the complexity of molecular regulation in renal tumorigenesis.